Summary/Abstract The goal of this project is to implement effective, imaging-based strategies combining DCE-MRI and DWI to assess response for breast cancer patients receiving pre-operative (neoadjuvant) chemotherapy. This project builds on the prior NCI Quantitative Imaging Network (QIN) U01 grant award CA151235 entitled ?Quantitative Imaging for Assessing Breast Cancer Response to Treatment? and addresses the needs for improved accuracy, standardization and consistency of breast MRI to perform quantitative assessment of treatment response across multiple clinical centers. The new QIN project will continue to advance quantitative MRI methods in the context of the I-SPY 2 TRIAL, an adaptive Phase II trial of targeted agents for breast cancer. We will use diagnostic models applied to the expanding I-SPY 2 cohorts to maximize the biomarker performance of imaging measurements and to construct decision tools to enable rational strategies for treatment modification. In prior work we developed and implemented image quality control and assessment processes for breast diffusion-weighted MRI (DWI) that were utilized in the American College of Radiology Imaging Network (ACRIN) trial 6698, an imaging sub-study of I-SPY 2 testing DWI for prediction of response. Initial results showed excellent repeatability of apparent diffusion coefficient (ADC) measurements using a standardized 4 b-value protocol, and change in ADC with treatment was found to be predictive of pathologic complete response (pCR). In parallel efforts, we worked with QIN collaborators at University of Michigan and industrial partners to develop gradient non-linearity correction and B0 inhomogeneity correction methods for ADC quantification. We also collaborated with the National Institute of Standards and Technology (NIST) to develop a universal breast MRI phantom for standardization of breast MRI in clinical trials. The new U01 project will evaluate these methods on the multiple vendor platforms in I-SPY 2 with particular focus on maximizing the combined performance of breast DCE-MRI and DWI. Under Specific Aim 1, we propose to gain performance improvements by implementing more advanced DWI pulse sequence techniques (multi b-value DWI and high spatial resolution DWI) and correcting known systematic errors (gradient non-linearity and B0 inhomogeneity). We will additionally implement a phantom-based quality assurance process to evaluate pulse sequence performance at all sites, with the goal of identifying and correcting platform bias and variability in ADC measurement and establishing quality benchmarks for data acceptance. Specific Aim 2 will focus on improving ADC quantitation by incorporating co-registration of DWI to dynamic contrast-enhanced (DCE) images, as well as automated segmentation techniques to measure heterogeneity in tumor ADC. We anticipate that these collective improvements in image acquisition, standardization, use of quality benchmarks and pixel- based metrics will lead to overall improvements in ADC measurement. The improved metrics will be tested in predictive models for pathologic response and survival in I-SPY 2.